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Soc. Sci. 2018, 7(1), 12; https://doi.org/10.3390/socsci7010012

Estimating Ideal Points from Roll-Call Data: Explore Principal Components Analysis, Especially for More Than One Dimension?

Department of Political Science and Social Science Research Institute, Duke University, Box 90420, Durham, NC 27708, USA
Received: 19 October 2017 / Revised: 27 December 2017 / Accepted: 3 January 2018 / Published: 12 January 2018
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Abstract

For two or more dimensions, the two main approaches to estimating legislators’ ideal points from roll-call data entail arbitrary, yet consequential, identification and modeling assumptions that bring about both indeterminateness and undue constraints for the ideal points. This paper presents a simple and fast approach to estimating ideal points in multiple dimensions that is not marred by those issues. The leading approach at present is that of Poole and Rosenthal. Also prominent currently is one that uses Bayesian techniques. However, in more than one dimension, they both have several problems, of which nonidentifiability of ideal points is the most precarious. The approach that we offer uses a particular mode of principal components analysis to estimate ideal points. It applies logistic regression to estimate roll-call parameters. It has a special feature that provides some guidance for deciding how many dimensions to use. Although its relative simplicity makes it useful even in just one dimension, its main advantages are for more than one. View Full-Text
Keywords: ideal points; nonidentifiability; Poole-Rosenthal scores; principal components analysis; spatial voting model; US Congress ideal points; nonidentifiability; Poole-Rosenthal scores; principal components analysis; spatial voting model; US Congress
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Potthoff, R.F. Estimating Ideal Points from Roll-Call Data: Explore Principal Components Analysis, Especially for More Than One Dimension? Soc. Sci. 2018, 7, 12.

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